genetic algorithms, soft-decision decoding, uniform crossover, Walsh polynomials
Soft-decision decoding is an NP-hard problem of great interest to developers of communication systems. We show that this problem is equivalent to the problem of optimizing Walsh polynomials. We present genetic algorithms for soft-decision decoding of binary linear block codes and compare the performance with various other decoding algorithms. Simulation results show that our algorithms achieve bit-error-probabilities as low as 0.00183 for a [104, 52] code with a low signal-to-noise ratio of 2.5 dB, exploring only 30,000 codewords, whereas the search space contains 4.5 x 1015 codewords. We define a new crossover operator that exploits domain-specific information and compare it with uniform and two point crossover. We also give a schema theorem for our genetic algorithm, showing that high reliability, low order codewords are the building blocks for the evolutionary process.
Maini, Harpal; Mehrotra, Kishan; Mohan, Chilukuri K.; and Ranka, Sanjay, "Genetic Algorithms for Soft Decision Decoding of Linear Block Codes" (1993). Electrical Engineering and Computer Science Technical Reports. Paper 161.